SLA-Based Adaptation Schemes in Distributed Stream Processing Engines
نویسندگان
چکیده
منابع مشابه
Benchmarking Distributed Stream Processing Engines
Over the last years, stream data processing has been gaining atten— tion both in industry and in academia due to its wide range of appli— cations. To fulfill the need for scalable and efficient stream analyt— ics, numerous open source stream data processing systems (SDPSs) have been developed, with high throughput and low latency being their key performance targets. In this paper, we propose a ...
متن کاملProgressive Recovery of Correlated Failures in Distributed Stream Processing Engines
Correlated failures in large-scale clusters have significant effects on systems’ availability, especially for streaming data applications that run continuously and require low processing latency. Most stateof-the-art distributed stream processing engines (DSPEs) adopt a blocking recovery paradigm, which, upon correlated failure, would block the progress of recovery until sufficient new resource...
متن کاملTuning QoD in stream processing engines
Quality of Service (QoS) and Quality of Data (QoD) are the two major dimensions for evaluating any query processing system. In the context of data stream management systems (DSMSs), multi-query scheduling has been exploited to improve QoS. In this paper, we are proposing to exploit query scheduling to improve QoD in DSMSs. Specifically, we are presenting a new policy for scheduling multiple con...
متن کاملThe Aurora and Borealis Stream Processing Engines
Over the last several years, a great deal of progress has been made in the area of stream-processing engines (SPEs) [9, 11, 17]. Three basic tenets distinguish SPEs from current data processing engines. First, they must support primitives for streaming applications. Unlike Online Transaction Processing (OLTP), which processes messages in isolation, streaming applications entail time series oper...
متن کاملFault Tolerance for Stream Processing Engines
Distributed Stream Processing Engines (DSPEs) target applications related to continuous computation, online machine learning and real-time query processing. DSPEs operate on high volume of data by applying lightweight operations on real-time and continuous streams. Such systems require clusters of hundreds of machine for their deployment. Streaming applications come with various requirements, i...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Applied Sciences
سال: 2019
ISSN: 2076-3417
DOI: 10.3390/app9061045